Why does GPT hallucinate?

The increasing interest in language models for practical applications has led to a proliferation of various models, with many organizations using GPT-4 through APIs provided by OpenAI and other companies. Despite their impressive fluency, these language models sometimes produce false statements ranging from minor inaccuracies to complete fabrications (als known as hallucinations), raising three primary concerns:

1. Accidental misuse: Insufficient testing may cause deployed models to give users false information, leading to deception and distrust.

2. Hindering positive applications: Strict accuracy requirements in fields like medicine and law may discourage the deployment of models without clear evidence of reliability, even if they have relevant knowledge.

3. Malicious misuse: Deceptive models generating plausible false statements can be exploited for disinformation or fraud.

By understanding why language models create false statements, we can develop more truthful models and assess deception risks.

Large language models, such as GPT-4, are trained on massive amounts of text data from diverse sources like books, articles, and websites. They learn to generate text by predicting the most suitable next word in a sequence based on patterns and relationships found in the training data.

However, this training process exposes the model to a mix of factual and fictional content. Consequently, when generating text, GPT-4 may struggle to differentiate between accurate information and false claims, leading to potential confabulation.

Confabulation, or hallucination, occurs when the model fills knowledge gaps with plausible-sounding words or phrases. GPT-4’s extensive training data enables it to create contextually appropriate and coherent text that appears credible. Still, this proficiency also means the model may inadvertently generate false or misleading information, even if it seems reasonable.

In essence, large language models like GPT-4 can generate seemingly informative and accurate text that may be entirely fabricated, as they are designed to predict and generate the most contextually appropriate words based on their training data. The challenge lies in distinguishing fact from fiction and addressing confabulation in the model’s output.

Several factors contribute to confabulation:

1. Inaccurate source material: Training datasets used for these models comprise a wide range of text sources, which may contain misconceptions, misinformation, or outdated facts. As a result, the model may learn and propagate inaccuracies when generating text.

2. Making inferences: When encountering an unfamiliar situation not covered in the training material, the model tries to generate text based on learned patterns and relationships. In doing so, the model may “hallucinate” or confabulate plausible but inaccurate information.

3. Model “temperature”: This parameter influences the creativity or randomness in the model’s text generation. High temperature encourages more creative and diverse outputs but may result in increased confabulation, while low temperature leads to more conservative and focused outputs that may be repetitive or less engaging.

Size matters

The observation that larger models tend to be less truthful, as seen in the “inverse scaling” trend, is intriguing since it contradicts the general notion in NLP that performance improves with model size. In a study by Lin et al., the researchers proposed two potential explanations:

1. Larger models produce more imitative falsehoods because they are better at learning the training distribution. As they more effectively capture patterns and relationships within diverse training data, they might inadvertently generate false statements that seem plausible based on the statistical relationships they have learned, resulting in a higher degree of confabulation compared to smaller models.

2. The questions being asked might be adversarially exploiting weaknesses in larger models, revealing issues not necessarily tied to the imitation of the training distribution. This means larger models may have certain biases or vulnerabilities that adversarial questions target, leading to less truthful responses.

How can we mitigate this?

To reduce confabulation in large language models like GPT-4, several strategies can be implemented:

1. Improve the training data: Ensure the training dataset is curated and cleaned, containing more accurate and reliable information. This will help the model learn better patterns and associations between words, leading to fewer confabulations.

2. Reinforcement Learning from Human Feedback (RLHF): Enhance the model’s learning by having human evaluators rank the model’s responses in order of preference. The feedback can be used to fine-tune the model, making it more aligned with the desired behavior and reducing confabulation.

3. Retrieval Augmentation: Train the model to access external sources like search engines or purpose-built databases to provide context and factual information. This would help the model generate responses based on reliable sources instead of relying solely on its training data.

4. Adjust the “temperature” or creativity setting: By controlling the model’s creativity level, its propensity for making wild guesses can be limited, reducing the chances of confabulation. However, it’s important to find the right balance, as lowering the creativity too much may result in overly conservative or repetitive responses.

5. Implement trust scoring: Link the training data to “trust” scores, using a method similar to PageRank, to help the model prioritize more reliable information during the generation process.

6. Train the model for self-awareness: Develop techniques that make the model aware of when it’s generating uncertain or unverified information, prompting it to provide more cautious or hedged responses.

In conclusion, although various strategies can be employed to mitigate confabulation in language models, it is crucial to acknowledge that these models inherently possess limitations. As a result, users must exercise due diligence when utilizing AI-generated text and verify the information presented instead of relying solely on the model’s output. By fostering a critical approach and continuously improving these models, we can harness the potential of AI-driven language generation while minimizing the risks associated with misinformation and deception.

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